These properties are used to configure tMahoutClustering running in the MapReduce Job framework.

The MapReduce
tMahoutClustering component belongs to the MapReduce family.

This component is available in Talend Platform products with Big Data and
in Talend Data Fabric.

The MapReduce framework is deprecated from Talend 7.3 onwards. Use Talend Jobs for Apache Spark to accomplish your integration tasks.

Basic settings

Global Variables

Usage

1. Drop the following components from the Palette onto the design workspace: tMahoutClustering, three tMap, two tAggregateRow and
   two tHDFSOutput components.
   

2. Set the components as shown in the capture and connect them together using
   Main links.

1. Click Run to open its view and then click the
   Hadoop Configuration tab to display its
   view for configuring the Hadoop connection for this Job.
2. From the Property type list,
   select Built-in. If you have created the
   connection to be used in Repository, then
   select Repository and thus the Studio will
   reuse that set of connection information for this Job.
3. In the Version area, select the
   Hadoop distribution to be used and its version.

   • If you use Google Cloud Dataproc, see Google Cloud Dataproc.

   • If you cannot
     find the Cloudera version to be used from this drop-down list, you can add your distribution
     via some dynamic distribution settings in the Studio.

   • If you cannot find from the list the distribution corresponding to
     yours, select Custom so as to connect to a
     Hadoop distribution not officially supported in the Studio. For a
     step-by-step example about how to use this
     Custom option, see Connecting to a custom Hadoop distribution.

4. In the Name node field, enter the location of
   the master node, the NameNode, of the distribution to be used. For example,
   hdfs://tal-qa113.talend.lan:8020.

   • If you are using a MapR distribution, you can simply leave maprfs:/// as it is in this field; then the MapR
     client will take care of the rest on the fly for creating the connection. The
     MapR client must be properly installed. For further information about how to set
     up a MapR client, see the following link in MapR’s documentation: http://doc.mapr.com/display/MapR/Setting+Up+the+Client

   • If you are using WebHDFS, the location should be
     webhdfs://masternode:portnumber; WebHDFS with SSL is not
     supported yet.

5. In the Resource Manager field,
   enter the location of the ResourceManager of your distribution. For example,
   tal-qa114.talend.lan:8050.

   • Then you can continue to set the following parameters depending on the
     configuration of the Hadoop cluster to be used (if you leave the check
     box of a parameter clear, then at runtime, the configuration about this
     parameter in the Hadoop cluster to be used will be ignored):

     • Select the Set resourcemanager
       scheduler address check box and enter the Scheduler address in
       the field that appears.

     • Select the Set jobhistory
       address check box and enter the location of the JobHistory
       server of the Hadoop cluster to be used. This allows the metrics information of
       the current Job to be stored in that JobHistory server.

     • Select the Set staging
       directory check box and enter this directory defined in your
       Hadoop cluster for temporary files created by running programs. Typically, this
       directory can be found under the yarn.app.mapreduce.am.staging-dir property in the configuration files
       such as yarn-site.xml or mapred-site.xml of your distribution.

     • Select the Use datanode hostname check box to allow the
       Job to access datanodes via their hostnames. This actually sets the dfs.client.use.datanode.hostname
       property to true. When connecting to a
       S3N filesystem, you must select this check box.

6. 
   If you are accessing the Hadoop cluster running
   with Kerberos security, select this check box, then, enter the Kerberos
   principal name for the NameNode in the field displayed. This enables you to use
   your user name to authenticate against the credentials stored in Kerberos.
   

   • If this cluster is a MapR cluster of the version 5.0.0 or later, you can set the
     MapR ticket authentication configuration in addition or as an alternative by following
     the explanation in Connecting to a security-enabled MapR.

     Keep in mind that this configuration generates a new MapR security ticket for the username
     defined in the Job in each execution. If you need to reuse an existing ticket issued for the
     same username, leave both the Force MapR ticket
     authentication check box and the Use Kerberos
     authentication check box clear, and then MapR should be able to automatically
     find that ticket on the fly.

   In addition, since this component performs Map/Reduce computations, you
   also need to authenticate the related services such as the Job history server and
   the Resource manager or Jobtracker depending on your distribution in the
   corresponding field. These principals can be found in the configuration files of
   your distribution. For example, in a CDH4 distribution, the Resource manager
   principal is set in the yarn-site.xml file and the Job history
   principal in the mapred-site.xml file.

   If you need to use a Kerberos keytab file to log in, select Use a keytab to authenticate. A keytab file contains
   pairs of Kerberos principals and encrypted keys. You need to enter the principal to
   be used in the Principal field and the access
   path to the keytab file itself in the Keytab
   field. This keytab file must be stored in the machine in which your Job actually
   runs, for example, on a Talend
   Jobserver.

   Note that the user that executes a keytab-enabled Job is not necessarily
   the one a principal designates but must have the right to read the keytab file being
   used. For example, the user name you are using to execute a Job is user1 and the principal to be used is guest; in this
   situation, ensure that user1 has the right to read the keytab
   file to be used.

7. In the User name field, enter the login user
   name for your distribution. If you leave it empty, the user name of the machine
   hosting the Studio will be used.
8. In the Temp folder field, enter the path in
   HDFS to the folder where you store the temporary files generated during
   Map/Reduce computations.
9. 
   Leave the default value of the Path separator in
   server as it is, unless you have changed the separator used by your
   Hadoop distribution’s host machine for its PATH variable or in other words, that
   separator is not a colon (:). In that situation, you must change this value to the
   one you are using in that host.
   
10. Leave the Clear temporary folder check box
    selected, unless you want to keep those temporary files.
11. Leave the Compress intermediate map output to reduce
    network traffic check box selected, so as to spend shorter time
    to transfer the mapper task partitions to the multiple reducers.
    However, if the data transfer in the Job is negligible, it is recommended to
    clear this check box to deactivate the compression step, because this
    compression consumes extra CPU resources.
12. If you need to use custom Hadoop properties, complete the Hadoop properties table with the property or
    properties to be customized. Then at runtime, these changes will override the
    corresponding default properties used by the Studio for its Hadoop
    engine.
    For further information about the properties required by Hadoop, see Apache’s
    Hadoop documentation on http://hadoop.apache.org, or
    the documentation of the Hadoop distribution you need to use.
13. 
    If the HDFS transparent encryption has been enabled in your cluster, select
    the Setup HDFS encryption configurations check
    box and in the HDFS encryption key provider field
    that is displayed, enter the location of the KMS proxy.
    

    For further information about the HDFS transparent encryption and its KMS proxy, see Transparent Encryption in HDFS.

14. You can tune the map and reduce computations by
    selecting the Set memory check box to set proper memory allocations
    for the computations to be performed by the Hadoop system.

    The memory parameters to be set are Map (in Mb),
    Reduce (in Mb) and ApplicationMaster (in Mb). These fields allow you to dynamically allocate
    memory to the map and the reduce computations and the ApplicationMaster of YARN.

    For further information about the Resource Manager, its scheduler and the
    ApplicationMaster, see YARN’s documentation such as http://hortonworks.com/blog/apache-hadoop-yarn-concepts-and-applications/.

    For further information about how to determine YARN and MapReduce memory configuration
    settings, see the documentation of the distribution you are using, such as the following
    link provided by Hortonworks: http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.6.0/bk_installing_manually_book/content/rpm-chap1-11.html.

15. 
    If you are using Cloudera V5.5+, you can select the Use Cloudera Navigator check box to enable the Cloudera Navigator
    of your distribution to trace your Job lineage to the component level, including the
    schema changes between components.
    

    With this option activated, you need to set the following parameters:

    • Username and Password: this is the credentials you use to connect to your Cloudera
      Navigator.

    • Cloudera Navigator URL : enter the location of the
      Cloudera Navigator to be connected to.

    • Cloudera Navigator Metadata URL: enter the location
      of the Navigator Metadata.

    • Activate the autocommit option: select this check box
      to make Cloudera Navigator generate the lineage of the current Job at the end of the
      execution of this Job.

      Since this option actually forces Cloudera Navigator to generate lineages of
      all its available entities such as HDFS files and directories, Hive queries or Pig
      scripts, it is not recommended for the production environment because it will slow the
      Job.

    • Kill the job if Cloudera Navigator fails: select this check
      box to stop the execution of the Job when the connection to your Cloudera Navigator fails.

      Otherwise, leave it clear to allow your Job to continue to run.

    • Disable SSL validation: select this check box to
      make your Job to connect to Cloudera Navigator without the SSL validation
      process.

      This feature is meant to facilitate the test of your Job but is not
      recommended to be used in a production cluster.

16. 
    If you are using Hortonworks Data Platform V2.4.0 onwards and you have
    installed Atlas in your cluster, you can select the Use
    Atlas check box to enable Job lineage to the component level, including the
    schema changes between components.
    

    With this option activated, you need to set the following parameters:

    • Atlas URL: enter the location of the Atlas to be
      connected to. It is often http://name_of_your_atlas_node:port

    • Die on error: select this check box to stop the Job
      execution when Atlas-related issues occur, such as connection issues to Atlas.

      Otherwise, leave it clear to allow your Job to continue to run.

    In the Username and Password fields, enter the authentication information for access to
    Atlas.

1. Double-click tMahoutClustering to open
   its Component view.
   

2. From the Schema list, select Built-In and then click the […] button next to Edit
   Schema and describe the data structure in the input
   file.
   

3. Add eight rows to the schema dialog box and define the input data as shown
   in the above capture.
   The component has one read-only column,
   clusterID.
4. Click OK.
5. In the File Configuration area:

   • Click the […] button next to
     the Input HDFS file and browse to
     the HDFS file on the Hadoop system that holds the input numerical
     data you want to cluster.

   • Set the field separator used to separate the columns in the
     clustered data.

   • In the Cluster columns table, add
     rows to the table and click in each row to select a column from the
     input schema.

6. In the Clustering Configuration
   area:

   • From the Clustering Type list,
     select what algorithm you want to use to cluster the numerical data,
     Fuzzy K-means in this
     example.

   • From the Distance Measure list,
     select the distance measure you want to use for clustering.

   • In the Number of clusters field,
     enter 3.

   • Leave the values in Max
     iterations and Convergence
     delta as they are.

1. Double-click tMap to open the Map Editor.
   

2. Drop the Region and the
   clusterID columns to the first output table that
   corresponds to the first tAggregateRow
   component.
   Drop the Channel and the
   clusterID columns to the second output table that
   corresponds to the second tAggregateRow
   component.
   Use the Schema editor section at the
   bottom of the editor to add necessary lines to the output tables.
3. Click OK to validate changes.

1. Double-click the first tAggregateRow to
   display its Basic settings view and define
   the component properties.
   

2. Click the […] button next to Edit schema and define the output flow.
   

3. Move the columns in the input schema to the output schema and then use the
   [+] button to add a new column in the
   output schema. Call it count.
   When done, click OK to close the dialog
   box.
4. In the Group by section, click the plus
   button to add an many lines as needed. Here you can define the group-by
   values.

   • Click in the first Output column
     row and select the output column that will hold the aggregated data,
     the region column in this example.

   • Click in the first Input column
     position row and select the input column from which
     you want to collect the values to be aggregated, the
     region column in this example.

5. In the Operations section, click the plus
   button to add rows for the columns that will hold the aggregated data. Here
   you can define the calculation values.

   • Click in the Output column row
     and select the destination column from the list, the
     count column in this example.

   • Click in the Function column row
     and select any of the listed operations.

     In this example, we want to count the number of clients, based on
     their regions, to be listed only once in the output column.

   • Click in the Input column
     position row and select the input column from which
     you want to collect the values to be aggregated, the
     region column in this example.

6. Double-click the second tAggregateRow
   component and define, the same way, its basic settings to count the number
   of clients in the second cluster based on the channel
   column.
   

1. Double-click the second tMap to open the
   Map Editor.
   

2. Drop the region, the clusterID
   and the count columns to the output table that
   corresponds to the first HDFS file.
3. Click OK to validate changes.
4. Double-click the third tMap to open the
   Map Editor.
   

5. Drop the channel, the clusterID
   and the count columns to the output table that
   corresponds to the second HDFS file.
6. Click OK to validate changes.

1. Double-click the first tHDFSOutput to
   open its Component view.
   

2. Click the […] button next to the
   Folder field and browse to the folder
   in which you want to write the region data.
3. From the Type list, select the data
   format for the records to be written. In this example, select Text file.
4. From the Action list, select the
   operation you need to perform on the file in question. If the file already
   exists, select Overwrite, otherwise select
   Create.
5. Select the Merge result to single file
   check box and enter the path, or browse to the file you need to write the
   merged output data in.
6. If the file for the merged data exists, select the Override target file check box to overwrite that
   file.
7. Double-click the second tHDFSOutput to
   open its Component view.
   

8. Define the component settings similarly to write the data about the client
   channels from the second cluster to an output HDFS folder.

1. Save your Job and press F6 to execute
   it.

   The below figure shows part of the clustered data written to the HDFS
   folders.

   tMahoutClustering reads data from the
   given Hadoop system and groups customer records into clusters. The other
   components in the Job analyze clustering results, show the number of
   customers in each cluster grouped by channels or regions and gives the
   cluster identification.

Visualizing output data

You can build a bar chart on each of the clusters to visualize the number of
customers grouped by different regions or channels.

Use tHDFSInput and tBarChart components in two Jobs to read the output HDFS
files and generate a bar chart on the data to ease technical
analysis.

In the first Job, tHDFSInput reads the
region output HDFS file and passes the flow to tBarChart. tBarChart reads
data from the input flow and transforms it into a bar chart in a PNG image
file.

In the second Job, tHDFSInput reads the
channel output HDFS file and passes the flow to tBarChart which transforms the input data into a bar chart
in a PNG image file.

Each bar chart has three columns, every column represents the number of
records in one cluster.

For further information about the tHDFSInput component,
see tHDFSInput and for more information about the
tBarChart, see tBarChart.